Principal Graph and Structure Learning Based on Reversed Graph Embedding
| Autor: | Yijun Sun, Li Wang, Qi Mao, Ivor W. Tsang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Theoretical computer science Graph embedding 02 engineering and technology Strength of a graph Machine learning computer.software_genre 03 medical and health sciences Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Artificial Intelligence & Image Processing Graph property Mathematics business.industry Applied Mathematics Voltage graph Directed graph 030104 developmental biology Computational Theory and Mathematics Graph (abstract data type) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Null graph computer Software Moral graph |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 39:2227-2241 |
| ISSN: | 2160-9292 0162-8828 |
| DOI: | 10.1109/tpami.2016.2635657 |
| Popis: | Many scientific datasets are of high dimension, and the analysis usually requires retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are mathematically formulated by curves, which is quite restrictive for real applications. A few methods can overcome the above problem, but they either require complicated human-made rules for a specific task with lack of adaption flexibility to different tasks, or cannot obtain explicit structures of data. To address these issues, we develop a novel principal graph and structure learning framework that captures the local information of the underlying graph structure based on reversed graph embedding. As showcases, models that can learn a spanning tree or a weighted undirected $\ell _1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously. The new algorithm is simple with guaranteed convergence. We then extend the proposed framework to deal with large-scale data. Experimental results on various synthetic and six real world datasets show that the proposed method compares favorably with baselines and can uncover the underlying structure correctly. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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